Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 112
... discriminant functions defined by Eq . ( 6 · 16 ) are com- prised of pieces of a number of " subsidiary " discriminant functions . These subsidiary functions are the gi ( X ) and the g2 ( X ) . Examination of Eq . ( i ) ( 6.13 ) reveals ...
... discriminant functions defined by Eq . ( 6 · 16 ) are com- prised of pieces of a number of " subsidiary " discriminant functions . These subsidiary functions are the gi ( X ) and the g2 ( X ) . Examination of Eq . ( i ) ( 6.13 ) reveals ...
Sivu 117
... subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the subsidiary ... discriminant functions while leaving their distributions within the banks fixed PIECEWISE LINEAR MACHINES 117.
... subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the subsidiary ... discriminant functions while leaving their distributions within the banks fixed PIECEWISE LINEAR MACHINES 117.
Sivu 118
... subsidiary linear discriminant functions have initially been se- lected arbitrarily . After presenting a pattern which the machine classifies correctly , we make no changes in the values of the weights used to imple- ment the subsidiary ...
... subsidiary linear discriminant functions have initially been se- lected arbitrarily . After presenting a pattern which the machine classifies correctly , we make no changes in the values of the weights used to imple- ment the subsidiary ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |